Design and Implementation of a High-Performance SAR ADC with Non-Standard Capacitor Arrays and Optimized Switching Control Logic
Bibliographic record
Abstract
The results in this thesis show the design of a SAR ADC made in a 130nm PDK with a non-standard capacitor array to have a smaller total layout area and higher ENOB while operating at a sampling rate of 10kS/s. Its design includes adding a clock-boosting circuit to the sample and hold circuit with an ENOB of 14.1, a VCDE to allow for higher sampled voltages to be converted to digital bit streams. The TSMC 130nm PDK was also used to for their standard cell library to create the SAR logic block to minimize the size of its total layout area. Schematic-level simulations of the SAR ADC report an ENOB of 9.5, a best-case DNL and INL of +0.65/-0.65 and +0.97/-0.96, respectively, average power consumption of 1.82μW, and a figure of merit (FOM) of 226.5 fJ/Conv after fixing a power consumption issue in the comparator with calibration schema’s SR latch.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".